Instabilities of nanofluid flow displacements in porous media
Bibliographic record
Abstract
Thanks to a number of advantageous characteristics, nanofluids are widely used in a variety of fluid flow systems. In porous media flows, the presence of nanoparticles can have dramatic effects on the flow dynamics and in particular on viscous fingering instabilities that develop when a less viscous fluid displaces a more viscous one. In the present study, these effects have been investigated both analytically and numerically using linear stability analysis (LSA) and non-linear simulations. The LSA problem was solved analytically using step function approximation, and general conclusions about the effects of nanofluids on the instability were derived from long wave expansion and cutoff wave number analyses. Furthermore, the quasi-steady-state approximation was used to expand the results of the LSA to diffusing initial concentration profiles, and simulations of the full non-linear problem have been carried out using a Hartley-transform based pseudo-spectral method. Results revealed that nanoparticles cannot make an otherwise stable flow unstable but can enhance or attenuate the instability of an originally unstable flow. In particular it was found that increases in the nanoparticles deposition rate or their rate of diffusion have both destabilizing effects. Furthermore, nanoparticles deposition can change the initial monotonically decreasing viscosity distribution to a non-monotonic one and results in the development of vortex dipoles. Analyses of vortex structures along with the viscosity distributions allowed to explain the observed trends and the resulting finger configurations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".